On Semifields of Type
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Littlewood–Richardson Coefficients and Birational Combinatorics
Littlewood{Richardson coefficients and birational combinatorics Darij Grinberg 28 August 2020 [corrected version] Algebraic and Combinatorial Perspectives in the Mathematical Sciences slides: http: //www.cip.ifi.lmu.de/~grinberg/algebra/acpms2020.pdf paper: arXiv:2008.06128 aka http: //www.cip.ifi.lmu.de/~grinberg/algebra/lrhspr.pdf 1 / 43 The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. 2 / 43 Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). 2 / 43 Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). 2 / 43 Chapter 1 Chapter 1 Littlewood{Richardson coefficients References (among many): Richard Stanley, Enumerative Combinatorics, vol. 2, Chapter 7. Darij Grinberg, Victor Reiner, Hopf Algebras in Combinatorics, arXiv:1409.8356. -
Kernel Methods for Knowledge Structures
Kernel Methods for Knowledge Structures Zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) von der Fakultät für Wirtschaftswissenschaften der Universität Karlsruhe (TH) genehmigte DISSERTATION von Dipl.-Inform.-Wirt. Stephan Bloehdorn Tag der mündlichen Prüfung: 10. Juli 2008 Referent: Prof. Dr. Rudi Studer Koreferent: Prof. Dr. Dr. Lars Schmidt-Thieme 2008 Karlsruhe Abstract Kernel methods constitute a new and popular field of research in the area of machine learning. Kernel-based machine learning algorithms abandon the explicit represen- tation of data items in the vector space in which the sought-after patterns are to be detected. Instead, they implicitly mimic the geometry of the feature space by means of the kernel function, a similarity function which maintains a geometric interpreta- tion as the inner product of two vectors. Knowledge structures and ontologies allow to formally model domain knowledge which can constitute valuable complementary information for pattern discovery. For kernel-based machine learning algorithms, a good way to make such prior knowledge about the problem domain available to a machine learning technique is to incorporate it into the kernel function. This thesis studies the design of such kernel functions. First, this thesis provides a theoretical analysis of popular similarity functions for entities in taxonomic knowledge structures in terms of their suitability as kernel func- tions. It shows that, in a general setting, many taxonomic similarity functions can not be guaranteed to yield valid kernel functions and discusses the alternatives. Secondly, the thesis addresses the design of expressive kernel functions for text mining applications. A first group of kernel functions, Semantic Smoothing Kernels (SSKs) retain the Vector Space Models (VSMs) representation of textual data as vec- tors of term weights but employ linguistic background knowledge resources to bias the original inner product in such a way that cross-term similarities adequately con- tribute to the kernel result. -
Geometry, Combinatorial Designs and Cryptology Fourth Pythagorean Conference
Geometry, Combinatorial Designs and Cryptology Fourth Pythagorean Conference Sunday 30 May to Friday 4 June 2010 Index of Talks and Abstracts Main talks 1. Simeon Ball, On subsets of a finite vector space in which every subset of basis size is a basis 2. Simon Blackburn, Honeycomb arrays 3. G`abor Korchm`aros, Curves over finite fields, an approach from finite geometry 4. Cheryl Praeger, Basic pregeometries 5. Bernhard Schmidt, Finiteness of circulant weighing matrices of fixed weight 6. Douglas Stinson, Multicollision attacks on iterated hash functions Short talks 1. Mari´en Abreu, Adjacency matrices of polarity graphs and of other C4–free graphs of large size 2. Marco Buratti, Combinatorial designs via factorization of a group into subsets 3. Mike Burmester, Lightweight cryptographic mechanisms based on pseudorandom number generators 4. Philippe Cara, Loops, neardomains, nearfields and sets of permutations 5. Ilaria Cardinali, On the structure of Weyl modules for the symplectic group 6. Bill Cherowitzo, Parallelisms of quadrics 7. Jan De Beule, Large maximal partial ovoids of Q−(5, q) 8. Bart De Bruyn, On extensions of hyperplanes of dual polar spaces 1 9. Frank De Clerck, Intriguing sets of partial quadrangles 10. Alice Devillers, Symmetry properties of subdivision graphs 11. Dalibor Froncek, Decompositions of complete bipartite graphs into generalized prisms 12. Stelios Georgiou, Self-dual codes from circulant matrices 13. Robert Gilman, Cryptology of infinite groups 14. Otokar Groˇsek, The number of associative triples in a quasigroup 15. Christoph Hering, Latin squares, homologies and Euler’s conjecture 16. Leanne Holder, Bilinear star flocks of arbitrary cones 17. Robert Jajcay, On the geometry of cages 18. -
Parametrizations of Canonical Bases and Totally Positive Matrices Arkady Berenstein
Advances in Mathematics AI1567 advances in mathematics 122, 49149 (1996) article no. 0057 Parametrizations of Canonical Bases and Totally Positive Matrices Arkady Berenstein Department of Mathematics, Northeastern University, Boston, Massachusetts 02115 Sergey Fomin* Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; and Theory of Algorithms Laboratory, St. Petersburg Institute of Informatics, Russian Academy of Sciences, St. Petersburg, Russia and Andrei Zelevinsky- Department of Mathematics, Northeastern University, Boston, Massachusetts 02115 Received October 24, 1995; accepted November 27, 1995 Contents. 1. Introduction and main results. 2. Lusztig variety and Chamber Ansatz. 2.1. Semifields and subtraction-free rational expressions. 2.2. Lusztig variety. 2.3. Pseudo-line arrangements. 2.4. Minors and the nilTemperleyLieb algebra. 2.5. Chamber Ansatz. 2.6. Solutions of the 3-term relations. 2.7. An alternative description of the Lusztig variety. 2.8. Trans- 0 n n ition from n . 2.9. Polynomials T J and Za . 2.10. Formulas for T J . 2.11. Sym- metries of the Lusztig variety. 3. Total positivity criteria. 3.1. Factorization of unitriangular matrices. 3.2. General- izations of Fekete's criterion. 3.3. Polynomials TJ (x). 3.4. The action of the four- group on N >0. 3.5. The multi-filtration in the coordinate ring of N. 3.6. The S3 _ZÂ2Z symmetry. 3.7. Dual canonical basis and positivity theorem. 4. Piecewise-linear minimization formulas. 4.1. Polynomials over the tropical semi- field. 4.2. Multisegment duality. 4.3. Nested normal orderings. 4.4. Quivers and boundary pseudo-line arrangements. 5. The case of an arbitrary permutation. -
2.2 Kernel and Range of a Linear Transformation
2.2 Kernel and Range of a Linear Transformation Performance Criteria: 2. (c) Determine whether a given vector is in the kernel or range of a linear trans- formation. Describe the kernel and range of a linear transformation. (d) Determine whether a transformation is one-to-one; determine whether a transformation is onto. When working with transformations T : Rm → Rn in Math 341, you found that any linear transformation can be represented by multiplication by a matrix. At some point after that you were introduced to the concepts of the null space and column space of a matrix. In this section we present the analogous ideas for general vector spaces. Definition 2.4: Let V and W be vector spaces, and let T : V → W be a transformation. We will call V the domain of T , and W is the codomain of T . Definition 2.5: Let V and W be vector spaces, and let T : V → W be a linear transformation. • The set of all vectors v ∈ V for which T v = 0 is a subspace of V . It is called the kernel of T , And we will denote it by ker(T ). • The set of all vectors w ∈ W such that w = T v for some v ∈ V is called the range of T . It is a subspace of W , and is denoted ran(T ). It is worth making a few comments about the above: • The kernel and range “belong to” the transformation, not the vector spaces V and W . If we had another linear transformation S : V → W , it would most likely have a different kernel and range. -
23. Kernel, Rank, Range
23. Kernel, Rank, Range We now study linear transformations in more detail. First, we establish some important vocabulary. The range of a linear transformation f : V ! W is the set of vectors the linear transformation maps to. This set is also often called the image of f, written ran(f) = Im(f) = L(V ) = fL(v)jv 2 V g ⊂ W: The domain of a linear transformation is often called the pre-image of f. We can also talk about the pre-image of any subset of vectors U 2 W : L−1(U) = fv 2 V jL(v) 2 Ug ⊂ V: A linear transformation f is one-to-one if for any x 6= y 2 V , f(x) 6= f(y). In other words, different vector in V always map to different vectors in W . One-to-one transformations are also known as injective transformations. Notice that injectivity is a condition on the pre-image of f. A linear transformation f is onto if for every w 2 W , there exists an x 2 V such that f(x) = w. In other words, every vector in W is the image of some vector in V . An onto transformation is also known as an surjective transformation. Notice that surjectivity is a condition on the image of f. 1 Suppose L : V ! W is not injective. Then we can find v1 6= v2 such that Lv1 = Lv2. Then v1 − v2 6= 0, but L(v1 − v2) = 0: Definition Let L : V ! W be a linear transformation. The set of all vectors v such that Lv = 0W is called the kernel of L: ker L = fv 2 V jLv = 0g: 1 The notions of one-to-one and onto can be generalized to arbitrary functions on sets. -
Non Associative Linear Algebras
NON ASSOCIATIVE LINEAR ALGEBRAS W. B. Vasantha Kandasamy Florentin Smarandache ZIP PUBLISHING Ohio 2012 This book can be ordered from: Zip Publishing 1313 Chesapeake Ave. Columbus, Ohio 43212, USA Toll Free: (614) 485-0721 E-mail: [email protected] Website: www.zippublishing.com Copyright 2012 by Zip Publishing and the Authors Peer reviewers: Sukanto Bhattacharya, Deakin Graduate School of Business, Deakin University, Australia Kuldeep Kumar, School of Business, Bond University, Australia Professor Paul P. Wang, Ph D, Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA Many books can be downloaded from the following Digital Library of Science: http://www.gallup.unm.edu/~smarandache/eBooks-otherformats.htm ISBN-13: 978-1-59973-176-6 EAN: 9781599731766 Printed in the United States of America 2 CONTENTS Preface 5 Chapter One BASIC CONCEPTS 7 Chapter Two NON ASSOCIATIVE SEMILINEAR ALGEBRAS 13 Chapter Three NON ASSOCIATIVE LINEAR ALGEBRAS 83 Chapter Four GROUPOID VECTOR SPACES 111 3 Chapter Five APPLICATION OF NON ASSOCIATIVE VECTOR SPACES / LINEAR ALGEBRAS 161 Chapter Six SUGGESTED PROBLEMS 163 FURTHER READING 225 INDEX 229 ABOUT THE AUTHORS 231 4 PREFACE In this book authors for the first time introduce the notion of non associative vector spaces and non associative linear algebras over a field. We construct non associative space using loops and groupoids over fields. In general in all situations, which we come across to find solutions may not be associative; in such cases we can without any difficulty adopt these non associative vector spaces/linear algebras. Thus this research is a significant one. -
Definition: a Semiring S Is Said to Be Semi-Isomorphic to the Semiring The
118 MA THEMA TICS: S. BO URNE PROC. N. A. S. that the dodecahedra are too small to accommodate the chlorine molecules. Moreover, the tetrakaidecahedra are barely long enough to hold the chlorine molecules along the polar axis; the cos2 a weighting of the density of the spherical shells representing the centers of the chlorine atoms.thus appears to be justified. On the basis of this structure for chlorine hydrate, the empirical formula is 6Cl2. 46H20, or C12. 72/3H20. This is in fair agreement with the gener- ally accepted formula C12. 8H20, for which Harris7 has recently provided further support. For molecules slightly smaller than chlorine, which could occupy the dodecahedra also, the predicted formula is M * 53/4H20. * Contribution No. 1652. 1 Davy, H., Phil. Trans. Roy. Soc., 101, 155 (1811). 2 Faraday, M., Quart. J. Sci., 15, 71 (1823). 3 Fiat Review of German Science, Vol. 26, Part IV. 4 v. Stackelberg, M., Gotzen, O., Pietuchovsky, J., Witscher, O., Fruhbuss, H., and Meinhold, W., Fortschr. Mineral., 26, 122 (1947); cf. Chem. Abs., 44, 9846 (1950). 6 Claussen, W. F., J. Chem. Phys., 19, 259, 662 (1951). 6 v. Stackelberg, M., and Muller, H. R., Ibid., 19, 1319 (1951). 7 In June, 1951, a brief description of the present structure was communicated by letter to Prof. W. H. Rodebush and Dr. W. F. Claussen. The structure was then in- dependently constructed by Dr. Claussen, who has published a note on it (J. Chem. Phys., 19, 1425 (1951)). Dr. Claussen has kindly informed us that the structure has also been discovered by H. -
The Kernel of a Derivation
Journal of Pure and Applied Algebra I34 (1993) 13-16 13 North-Holland The kernel of a derivation H.G.J. Derksen Department of Mathematics, CathoCk University Nijmegen, Nijmegen, The h’etherlands Communicated by C.A. Weibel Receircd 12 November 1991 Revised 22 April 1992 Derksen, H.G.J., The kernel of a deriv+ Jn, Journal of Pure and Applied Algebra 84 (1993) 13-i6. Let K be a field of characteristic 0. Nagata and Nowicki have shown that the kernel of a derivation on K[X, , . , X,,] is of F.&e type over K if n I 3. We construct a derivation of a polynomial ring in 32 variables which kernel is not of finite type over K. Furthermore we show that for every field extension L over K of finite transcendence degree, every intermediate field which is algebraically closed in 6. is the kernel of a K-derivation of L. 1. The counterexample In this section we construct a derivation on a polynomial ring in 32 variables, based on Nagata’s counterexample to the fourteenth problem of Hilbert. For the reader’s convenience we recari the f~ia~~z+h problem of iX~ert and Nagata’:: counterexample (cf. [l-3]). Hilbert’s fourteenth problem. Let L be a subfield of @(X,, . , X,, ) a Is the ring Lnc[x,,.. , X,] of finite type over @? Nagata’s counterexample. Let R = @[Xl, . , X,, Y, , . , YrJ, t = 1; Y2 - l = yy and vi =tXilYi for ~=1,2,. ,r. Choose for j=1,2,3 and i=1,2,. e ,I ele- ments aj i E @ algebraically independent over Q. -
Lecture 7.3: Ring Homomorphisms
Lecture 7.3: Ring homomorphisms Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4120, Modern Algebra M. Macauley (Clemson) Lecture 7.3: Ring homomorphisms Math 4120, Modern algebra 1 / 10 Motivation (spoilers!) Many of the big ideas from group homomorphisms carry over to ring homomorphisms. Group theory The quotient group G=N exists iff N is a normal subgroup. A homomorphism is a structure-preserving map: f (x ∗ y) = f (x) ∗ f (y). The kernel of a homomorphism is a normal subgroup: Ker φ E G. For every normal subgroup N E G, there is a natural quotient homomorphism φ: G ! G=N, φ(g) = gN. There are four standard isomorphism theorems for groups. Ring theory The quotient ring R=I exists iff I is a two-sided ideal. A homomorphism is a structure-preserving map: f (x + y) = f (x) + f (y) and f (xy) = f (x)f (y). The kernel of a homomorphism is a two-sided ideal: Ker φ E R. For every two-sided ideal I E R, there is a natural quotient homomorphism φ: R ! R=I , φ(r) = r + I . There are four standard isomorphism theorems for rings. M. Macauley (Clemson) Lecture 7.3: Ring homomorphisms Math 4120, Modern algebra 2 / 10 Ring homomorphisms Definition A ring homomorphism is a function f : R ! S satisfying f (x + y) = f (x) + f (y) and f (xy) = f (x)f (y) for all x; y 2 R: A ring isomorphism is a homomorphism that is bijective. The kernel f : R ! S is the set Ker f := fx 2 R : f (x) = 0g. -
7. Quotient Groups III We Know That the Kernel of a Group Homomorphism Is a Normal Subgroup
7. Quotient groups III We know that the kernel of a group homomorphism is a normal subgroup. In fact the opposite is true, every normal subgroup is the kernel of a homomorphism: Theorem 7.1. If H is a normal subgroup of a group G then the map γ : G −! G=H given by γ(x) = xH; is a homomorphism with kernel H. Proof. Suppose that x and y 2 G. Then γ(xy) = xyH = xHyH = γ(x)γ(y): Therefore γ is a homomorphism. eH = H plays the role of the identity. The kernel is the inverse image of the identity. γ(x) = xH = H if and only if x 2 H. Therefore the kernel of γ is H. If we put all we know together we get: Theorem 7.2 (First isomorphism theorem). Let φ: G −! G0 be a group homomorphism with kernel K. Then φ[G] is a group and µ: G=H −! φ[G] given by µ(gH) = φ(g); is an isomorphism. If γ : G −! G=H is the map γ(g) = gH then φ(g) = µγ(g). The following triangle summarises the last statement: φ G - G0 - γ ? µ G=H Example 7.3. Determine the quotient group Z3 × Z7 Z3 × f0g Note that the quotient of an abelian group is always abelian. So by the fundamental theorem of finitely generated abelian groups the quotient is a product of abelian groups. 1 Consider the projection map onto the second factor Z7: π : Z3 × Z7 −! Z7 given by (a; b) −! b: This map is onto and the kernel is Z3 ×f0g. -
1 Semifields
1 Semifields Definition 1 A semifield P = (P; ⊕; ·): 1. (P; ·) is an abelian (multiplicative) group. 2. ⊕ is an auxiliary addition: commutative, associative, multiplication dis- tributes over ⊕. Exercise 1 Show that semi-field P is torsion-free as a multiplicative group. Why doesn't your argument prove a similar result about fields? Exercise 2 Show that if a semi-field contains a neutral element 0 for additive operation and 0 is multiplicatively absorbing 0a = a0 = 0 then this semi-field consists of one element Exercise 3 Give two examples of non injective homomorphisms of semi-fields Exercise 4 Explain why a concept of kernel is undefined for homorphisms of semi-fields. A semi-field T ropmin as a set coincides with Z. By definition a · b = a + b, T rop a ⊕ b = min(a; b). Similarly we define T ropmax. ∼ Exercise 5 Show that T ropmin = T ropmax Let Z[u1; : : : ; un]≥0 be the set of nonzero polynomials in u1; : : : ; un with non- negative coefficients. A free semi-field P(u1; : : : ; un) is by definition a set of equivalence classes of P expression Q , where P; Q 2 Z[u1; : : : ; un]≥0. P P 0 ∼ Q Q0 if there is P 00;Q00; a; a0 such that P 00 = aP = a0P 0;Q00 = aQ = a0Q0. 1 0 Exercise 6 Show that for any semi-field P and a collection v1; : : : ; vn there is a homomorphism 0 : P(u1; : : : ; un) ! P ; (ui) = vi Let k be a ring. Then k[P] is the group algebra of the multiplicative group of the semi-field P.